Simulating the evolution of glyphosate resistance in grains farming in northern Australia

Thornby, David; Walker, S.

doi:10.1093/aob/mcp152

Cited by 33 publications

(36 citation statements)

References 15 publications

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“…Parsons et al, 2009;Benjamin et al, 2010;Gardarin et al, 2012;Peltzer et al, 2012;Lacoste and Powles, 2014;Storkey et al, 2015;Department of Agriculture andFood Western Australia, 2015, Renton et al, 2015). Some of these advances are being made by integrating the crop simulation models, such as APSIM, with other models that handle the weed population dynamics (Smith et al, 2005;Thornby et al, 2006;Thornby and Walker, 2009;Lawes and Renton, 2015;Holzworth et al, 2014). An offshoot of these weed population dynamics models is a class of models that consider evolution and selection in weed populations over generations, so far focussed on herbicide resistance traits .…”

Section: How Has Crop-weed Competition Been Modelled?mentioning

confidence: 99%

Modelling crop-weed competition: Why, what, how and what lies ahead?

Renton

Chauhan

2017

Crop Protection

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Section: How Has Crop-weed Competition Been Modelled?mentioning

confidence: 99%

Modelling crop-weed competition: Why, what, how and what lies ahead?

Renton

Chauhan

2017

Crop Protection

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“…With population densities comes the need to consider density‐dependent nature of seed production, self‐thinning and mortality, and intra‐ and interspecific resource competition. These have often been represented in fairly simple descriptive ways, such as using a hyperbolic competition function like that developed by Firbank and Watkinson, where seed production per unit area tends towards a fixed maximum at high densities (see Renton et al and Diggle et al for examples), although more recently resistance evolution models have been linked with more complex crop growth simulation models that handle the simulation of crop–weed competition with much greater temporal and spatial resolution and biological realism …”

Section: Approaches To Modelling the Evolution Of Resistancementioning

confidence: 99%

Herbicide resistance modelling: past, present and future

Renton

Busi

Neve

et al. 2014

Pest Management Science

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Computer simulation modelling is an essential aid in building an integrated understanding of how different factors interact to affect the evolutionary and population dynamics of herbicide resistance, and thus in helping to predict and manage how agricultural systems will be affected. In this review, we first discuss why computer simulation modelling is such an important tool and framework for dealing with herbicide resistance. We then explain what questions related to herbicide resistance have been addressed to date using simulation modelling, and discuss the modelling approaches that have been used, focusing first on the earlier, more general approaches, and then on some newer, more innovative approaches. We then consider how these approaches could be further developed in the future, by drawing on modelling techniques that are already employed in other areas, such as individual-based and spatially explicit modelling approaches, as well as the possibility of better representing genetics, competition and economics, and finally the questions and issues of importance to herbicide resistance research and management that could be addressed using these new approaches are discussed. We conclude that it is necessary to proceed with caution when increasing the complexity of models by adding new details, but, with appropriate care, more detailed models will make it possible to integrate more current knowledge in order better to understand, predict and ultimately manage the evolution of herbicide resistance.

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“…Data for the adapted model originated from three Australian GR populations, described in Powles et al ,107 Pratley et al 18 and Neve et al 33 The assumption of random mating in outcrossing species was a noted simplification; limited pollen flow will ensure preferential mating between neighbours 8. Future model versions should incorporate spatial structure and processes so that implications of spatial aggregation and gene flow within and between populations can be better determined 8, 108…”

Section: Management Strategies and Practicesmentioning

confidence: 99%

Herbicide‐resistant weed management: focus on glyphosate

Beckie

2011

Pest Management Science

182

142

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This review focuses on proactive and reactive management of glyphosate-resistant (GR) weeds. Glyphosate resistance in weeds has evolved under recurrent glyphosate usage, with little or no diversity in weed management practices. The main herbicide strategy for proactively or reactively managing GR weeds is to supplement glyphosate with herbicides of alternative modes of action and with soil-residual activity. These herbicides can be applied in sequences or mixtures. Proactive or reactive GR weed management can be aided by crop cultivars with alternative single or stacked herbicide-resistance traits, which will become increasingly available to growers in the future. Many growers with GR weeds continue to use glyphosate because of its economical broad-spectrum weed control. Government farm policies, pesticide regulatory policies and industry actions should encourage growers to adopt a more proactive approach to GR weed management by providing the best information and training on management practices, information on the benefits of proactive management and voluntary incentives, as appropriate. Results from recent surveys in the United States indicate that such a change in grower attitudes may be occurring because of enhanced awareness of the benefits of proactive management and the relative cost of the reactive management of GR weeds.

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Simulating the evolution of glyphosate resistance in grains farming in northern Australia

Cited by 33 publications

References 15 publications

Modelling crop-weed competition: Why, what, how and what lies ahead?

Modelling crop-weed competition: Why, what, how and what lies ahead?

Herbicide resistance modelling: past, present and future

Herbicide‐resistant weed management: focus on glyphosate

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